The use of recombining genetic markers for demographic inference

描述(申请人提供)：拟议的研究涉及对历史移民模式的推断。传统的移民人口遗传模型假设，人口在很长一段时间内一直在以恒定的速度交换移民。然而，对于许多物种来说，这种假设可能并不合适。因此，建议开发一种计算方法来检验最近迁移率的变化并估计相关的人口参数。虽然大多数人口统计推断方法都假设所有正在研究的遗传标记是独立的(无关联的)，但这种方法将利用重组染色体上的连锁模式。通过考虑这种关联信息(具体地说，推断出有移民血统的DNA片段的长度)，人们可以超越估计两个种群之间发生了多少迁移，并说出一些关于历史上这种迁移发生的时间。在这个项目的第一阶段，将利用现有的模拟程序(MS)和推理方法(Structure 2.0)来研究不同种群历史对迁徙DNA片段长度分布的影响。接下来，将在最大似然或贝叶斯框架下使用马尔科夫链蒙特卡罗方法来开发上述新的推理方法。最后，将该方法应用于现有的人类多态数据集(SNP和微卫星)，以检验自人类种群分化以来人类种群之间的迁移一直是恒定的零假设。这一分析将允许估计混合人群的人口学参数，因此将有助于为疾病关联的混合图谱研究选择人群。 与公共卫生的相关性：拟议研究的目标是测试人口间迁移率的历史变化，并估计迁移率变化以来的时间和这种变化的幅度等数量。开发的计算方法将有多种应用，包括估计有最近混血历史(来自多种来源的祖先)的人口中的人口参数，如非裔美国人、西班牙裔、中亚和北非人口。这些信息将有助于评估这类人群对混合图谱研究的效用，该研究旨在确定与复杂疾病相关的遗传变异，这些疾病在不同人群中发生的频率不同。

DESCRIPTION (provided by applicant): The proposed research concerns the inference of historical patterns of migration. Traditional population genetic models of migration assume that populations have been exchanging migrants at a constant rate over long periods of time. For many species, however, this assumption may not be appropriate. Therefore, the development of a computational method to test for recent changes in migration rate and to estimate the relevant demographic parameters is proposed. While most methods of demographic inference assume that all of the genetic markers being studied are independent (unlinked), this approach will take advantage of the patterns of linkage along a recombining chromosome. By considering this linkage information (specifically, the lengths of DNA segments that inferred to have migrant origin), one can go beyond estimating how much migration has occurred between two populations, and say something about when, historically, this migration occurred. During the first phase of this project, the effect of various population histories on the length distribution of migrant DNA segments will be investigated, making use of an existing simulation program (ms) and inference method (structure 2.0). Next, the new inference method described above will be developed, using Markov chain Monte Carlo methodology in a maximum likelihood or Bayesian framework. Finally, this method will be applied to existing human polymorphism data sets (both SNP and microsatellite) in order to test the null hypothesis that migration among human populations has been constant since their divergence. This analysis will permit the estimation of demographic parameters for admixed human populations, and will therefore aid in the selection of populations for admixture mapping studies of disease association. Relevance to public health: The goal of the proposed research is to test for historical changes in the rate of migration between populations, and to estimate quantities such as the time since a migration rate change and the magnitude of such a change. The computational method developed will have a variety of applications, including the estimation of demographic parameters in human populations with a history of recent admixture (ancestry from multiple sources), such as African-American, Hispanic, Central Asian and Northern African populations. That information will be relevant in assessing the utility of such populations for admixture mapping studies, which aim to identify genetic variants associated with complex diseases that occur at different frequencies in different populations.